Download PDFOpen PDF in browserFault Location Technology of DC Control and Protection System Based on Deep LearningEasyChair Preprint 150675 pages•Date: September 25, 2024AbstractPower systems sometimes experience various types of faults, and fault location in power systems has become increasingly complex with the growing complexity of distribution networks and the diversity of measurement data. This paper proposes a fault location method for power systems based on a Graph Convolutional Neural Network (GCN) and incorporates an attention mechanism to further improve the accuracy and stability of fault location. By collecting real power grid data, fault data is simulated for model training and testing. Measurement points are treated as nodes in the graph, and node connections are constructed based on the power grid structure. The GNN is used to interact node information, while a Transformer model with an attention mechanism aggregates and predicts the information. Additionally, the paper compares the proposed model with several existing methods, demonstrating the accuracy and stability of the model for fault location in power systems. Keyphrases: GCN, deep learning, fault location, power system, transformer
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